{"id":"https://openalex.org/W4392971698","doi":"https://doi.org/10.3233/faia240843","title":"Informed Spectral Normalized Gaussian Processes for Trajectory Prediction","display_name":"Informed Spectral Normalized Gaussian Processes for Trajectory Prediction","publication_year":2024,"publication_date":"2024-10-16","ids":{"openalex":"https://openalex.org/W4392971698","doi":"https://doi.org/10.3233/faia240843"},"language":"en","primary_location":{"id":"doi:10.3233/faia240843","is_oa":true,"landing_page_url":"https://doi.org/10.3233/faia240843","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"},"type":"book-chapter","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.3233/faia240843","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5021767452","display_name":"Christian Schlauch","orcid":null},"institutions":[{"id":"https://openalex.org/I147869694","display_name":"Continental (Germany)","ror":"https://ror.org/0359s0245","country_code":"DE","type":"company","lineage":["https://openalex.org/I147869694"]},{"id":"https://openalex.org/I39343248","display_name":"Humboldt-Universit\u00e4t zu Berlin","ror":"https://ror.org/01hcx6992","country_code":"DE","type":"education","lineage":["https://openalex.org/I39343248"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Christian Schlauch","raw_affiliation_strings":["Continental Automotive Technologies GmbH, AI Lab Berlin","Humboldt-Universit\u00e4t zu Berlin"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Continental Automotive Technologies GmbH, AI Lab Berlin","institution_ids":["https://openalex.org/I147869694"]},{"raw_affiliation_string":"Humboldt-Universit\u00e4t zu Berlin","institution_ids":["https://openalex.org/I39343248"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034705495","display_name":"Christian Wirth","orcid":"https://orcid.org/0000-0003-2604-8056"},"institutions":[{"id":"https://openalex.org/I147869694","display_name":"Continental (Germany)","ror":"https://ror.org/0359s0245","country_code":"DE","type":"company","lineage":["https://openalex.org/I147869694"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Christian Wirth","raw_affiliation_strings":["Continental Automotive Technologies GmbH, AI Lab Berlin"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Continental Automotive Technologies GmbH, AI Lab Berlin","institution_ids":["https://openalex.org/I147869694"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009563869","display_name":"Nadja Klein","orcid":"https://orcid.org/0000-0002-5072-5347"},"institutions":[{"id":"https://openalex.org/I102335020","display_name":"Karlsruhe Institute of Technology","ror":"https://ror.org/04t3en479","country_code":"DE","type":"education","lineage":["https://openalex.org/I102335020","https://openalex.org/I1305996414"]},{"id":"https://openalex.org/I4210143490","display_name":"Computing Center","ror":"https://ror.org/0557kgc34","country_code":"RU","type":"facility","lineage":["https://openalex.org/I1313323035","https://openalex.org/I4210143490","https://openalex.org/I4210148470"]}],"countries":["DE","RU"],"is_corresponding":false,"raw_author_name":"Nadja Klein","raw_affiliation_strings":["Karlsruhe Institute of Technology, Scientific Computing Center, Methods for Big Data"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Karlsruhe Institute of Technology, Scientific Computing Center, Methods for Big Data","institution_ids":["https://openalex.org/I102335020","https://openalex.org/I4210143490"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.1397,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.84727444,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.995199978351593,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.995199978351593,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12095","display_name":"Vehicle emissions and performance","score":0.9911999702453613,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9822999835014343,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.7517485618591309},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.6046435832977295},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.5948983430862427},{"id":"https://openalex.org/keywords/statistical-physics","display_name":"Statistical physics","score":0.515554666519165},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.41063234210014343},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.3572709560394287},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3323085308074951},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3259665071964264},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.2923281192779541},{"id":"https://openalex.org/keywords/quantum-mechanics","display_name":"Quantum mechanics","score":0.07148575782775879}],"concepts":[{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.7517485618591309},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.6046435832977295},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.5948983430862427},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.515554666519165},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.41063234210014343},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3572709560394287},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3323085308074951},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3259665071964264},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.2923281192779541},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.07148575782775879}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3233/faia240843","is_oa":true,"landing_page_url":"https://doi.org/10.3233/faia240843","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"},{"id":"pmh:oai:arXiv.org:2403.11966","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2403.11966","pdf_url":"https://arxiv.org/pdf/2403.11966","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.3233/faia240843","is_oa":true,"landing_page_url":"https://doi.org/10.3233/faia240843","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W1941703695","https://openalex.org/W3131574667","https://openalex.org/W4323768008","https://openalex.org/W4248382324","https://openalex.org/W4360995134","https://openalex.org/W2039473718","https://openalex.org/W2387529410","https://openalex.org/W82723519","https://openalex.org/W1964286703","https://openalex.org/W2169866437"],"abstract_inverted_index":{"Prior":[0],"parameter":[1,110],"distributions":[2],"provide":[3],"an":[4,149],"elegant":[5],"way":[6],"to":[7,24,118,156],"represent":[8,91],"prior":[9,92,128],"expert":[10],"knowledge":[11,93],"for":[12,42,81],"informed":[13,115,161],"learning.":[14],"Previous":[15],"work":[16],"has":[17],"shown":[18],"that":[19,90],"using":[20],"such":[21],"informative":[22,88],"priors":[23,89],"regularize":[25],"probabilistic":[26,43],"deep":[27],"learning":[28,79],"(DL)":[29],"models":[30,45],"increases":[31],"their":[32],"performance":[33,138],"and":[34,54,104,143,146,154,160],"data":[35,142,152],"efficiency.":[36],"However,":[37],"commonly":[38],"used":[39],"sampling-based":[40],"approximations":[41,66],"DL":[44],"can":[46],"be":[47],"computationally":[48],"expensive,":[49],"requiring":[50],"multiple":[51],"forward":[52],"passes":[53],"longer":[55],"training":[56,141],"times.":[57],"Promising":[58],"alternatives":[59],"are":[60],"compute":[61],"efficient":[62],"last":[63],"layer":[64],"kernel":[65],"like":[67],"spectral":[68],"normalized":[69],"Gaussian":[70],"processes":[71],"(SNGPs).":[72],"We":[73,112],"propose":[74],"a":[75],"novel":[76],"regularization-based":[77],"continual":[78],"method":[80],"SNGPs,":[82],"which":[83],"enables":[84],"the":[85,119],"use":[86],"of":[87],"learned":[94],"from":[95],"previous":[96],"tasks.":[97],"Our":[98],"proposal":[99],"builds":[100],"upon":[101],"well-established":[102],"methods":[103],"requires":[105],"no":[106],"rehearsal":[107],"memory":[108],"or":[109],"expansion.":[111],"apply":[113],"our":[114],"SNGP":[116],"model":[117],"trajectory":[120],"prediction":[121],"problem":[122],"in":[123,151],"autonomous":[124],"driving":[125],"by":[126],"integrating":[127],"drivability":[129],"knowledge.":[130],"On":[131],"two":[132],"public":[133],"datasets,":[134],"we":[135],"investigate":[136],"its":[137],"under":[139],"diminishing":[140],"across":[144],"locations,":[145],"thereby":[147],"demonstrate":[148],"increase":[150],"efficiency":[153],"robustness":[155],"location-transfers":[157],"over":[158],"non-informed":[159],"baselines.":[162]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2026-08-26T07:47:46.906454","created_date":"2025-10-10T00:00:00"}